1. Field of the Invention
The invention relates an aircraft-based infrared image recognition device for a ground moving target.
2. Description of the Related Art
An aircraft is a fast moving platform. Its movement (“platform movement”) causes a rapid change of the terrain (“background image”) its capturing equipment can detect. When an aircraft tracks a target (“foreground image”), background variation is due to both, the platform movement and the foreground image movement.
There are two basic types of algorithms for separating background variation into these two components. The first type is static-platform-based target detection and tracing algorithm; the second type is moving-platform-based target detection and tracing algorithm. Compared with the static-platform-based target detection and tracing algorithm, the moving-platform-based target detection and tracing algorithm is much more complex.
A scale-invariant feature transform (SIFT) algorithm is a classical and effective image registration algorithm that can be used for distinguishing foreground movement from background movement. However, the algorithm features heavy computation burden, which makes it difficult to enable real-time registration as it is executed on a single digital signal processor (DSP). In addition, detection and tracing of a ground target may face certain problems like complex background, interference (such as shielding) and so on.
Automatic target recognition of an infrared image of an aircraft is a process in which an imaging platform moves from far to near with respect to a target. A long-distance target is mainly a point target with little information. A medium-distance target is mainly a speckle target, and a size, a shape and grayscale distribution of an image can be utilized. For a comparatively short-distance target (which represents an area target), very detailed feature information (comprising rich shape and texture features) can be obtained and used for recognition classification. Correspondingly, target feature models and target recognition algorithms reflect characteristics of multi-level and multi-dimension. Therefore, feature space for feature extraction and mapping and target representation should be graded for adequate target information mining at different phases. However, common recognition algorithms cannot handle the searching, detection, and recognition processes, and multi-state recognition processes under long-distance, medium-distance and short-distance imaging have been developed, so that a processing system is capable of correctly detecting, tracing and recognizing a target. This further increases workload of the system, as shown in FIG. 1.
(1) Long-distance imaging: normally at the beginning of target recognition, a scene is obtained at a comparatively far height or distance so that a resulting view field is much wider, and a target is a small point target with no shape. By employing algorithms such as a matched filtering algorithm, a multistage filtering algorithm and so on, it is possible to suppress interference of background and noise in two-dimensional space or three-dimensional time-space space and highlight the target, thereby capturing the target.
(2) Medium-distance imaging: the aircraft may approach the target and enters a tracing phase after capturing the target. During the tracing phase, a window can be properly set for relieving computation burden. At this time, the target is a speckle target with certain shape information. To distinguish background movement from foreground movement, a SIFT operator is used for feature extraction and image registration. Then a multi-level filter is used for highlighting the target and suppressing background clutter.
(3) Short-distance imaging: as the aircraft continues approaching the target, the target as an area target indicates more feature information regarding profile and texture. At this time, a connected domain labeling and profile tracing algorithm can be used for tracing the target, and the SIFT operator can be used for extracting feature, thereby enabling image registration, mapping and recognition of the texture of the target, and finally recognition of the target.
A conventional aircraft-based system for processing an infrared image normally is limited by volume, weight, power consumption, and so on, and meanwhile, the processing algorithm is comparatively complex. Therefore, a parallel processor with high computing capability and flexibility needs to be designed for ensuring real-time performance during computation. Thus, there are several requirements for the processor:
(1) real-time performance: only real-time target recognition can ensure accurate tracing and positioning of a target during movement of an aircraft as a platform, since target recognition of the aircraft aims to guide the aircraft to detect and trace the target, and the aircraft normally features a comparatively high flight speed;
(2) size reduction: size reduction trend of an aircraft requires an infrared image processing system has smaller size and the same or even more functions as compared with a conventional system;
(3) low power consumption: size reduction of an aircraft may cause a problem of heat dissipation; in this scenario, an infrared image processing system with low power consumption has to be designed to address the problem thereby ensuring reliability of the system in operation.
A conventional infrared image processing system mainly employs a structure combining a DSP with a field programmable gate array (FPGA), or that combining multiple DSPs and FPGAs. However, this kind of isomorphic structure has some disadvantages such as high power consumption and low efficiency, moreover, a bottleneck problem in terms of optimization of image processing and target recognition exists in the DSP processor due to its generality.